TGF-β generates a population of cancer cells residing in G1 phase with high motility and metastatic potential via KRTAP2-3

Takahashi, Kuniyuki; Podyma‐Inoue, Katarzyna Α.; Saito, Maki; Sakakitani, Shintaro; Sugauchi, Akinari; Iida, Keita; Iwabuchi, Sadahiro; Koinuma, Daizo; Kurioka, Kyoko; Konishi, Teruko; Tanaka, Susumu; Kaida, Atsushi; Miura, Masahiko; Hashimoto, Shinichi; Okada, Mariko; Uchihashi, Toshihiro; Miyazono, Kohei; Watabe, Tetsuro

doi:10.1016/j.celrep.2022.111411

Cited by 18 publications

(13 citation statements)

References 52 publications

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“…We assessed the trends in G1 phase of cell cycle stages of time-dependent data during TGF β treatment using Kendall Tau statistic 28 and found that in general the G1 stage positively trends for TGF β treated cells during treatment (day 0 to 7) as shown in Figure 6A, consistent with previous reports 52 . However, this trend was stronger for the two cell lines with lower mean KS scores (MCF7: 0.6, and OVCA420: 0.8) than DU145 and A549 (DU145: −0.2, and A549: 0.4) (Figure 6A).…”

Section: Resultssupporting

confidence: 88%

Population Dynamics of EMT Elucidates the Timing and Distribution of Phenotypic Intra-tumoral Heterogeneity

Najafi¹,

Jolly

George

2023

Preprint

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The Epithelial-to-Mesenchymal Transition (EMT) is a hallmark of cancer metastasis and morbidity. EMT is a non-binary process, and cells can be stably arrested en route to EMT in an intermediate hybrid state associated with enhanced tumor aggressiveness and worse patient outcomes. Understand- ing EMT progression in detail will provide fundamental insights into the mechanisms underlying metastasis. Despite increasingly available single-cell RNA sequencing data that enable in-depth analyses of EMT at the single-cell resolution, current inferential approaches are limited to bulk microarray data. There is thus a great need for computational frameworks to systematically infer and predict the timing and distribution of EMT-related states at single-cell resolution. Here, we develop a computational framework for reliable inference and prediction of EMT-related trajectories from single-cell RNA sequencing data. Our model can be utilized across a variety of applications to predict the timing and distribution of EMT from single-cell sequencing data.

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Section: Resultssupporting

confidence: 88%

Population Dynamics of EMT Elucidates the Timing and Distribution of Phenotypic Intra-tumoral Heterogeneity

Najafi¹,

Jolly

George

2023

Preprint

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“…Moreover, another keratin gene, KRTAP2-3 , also exerted increased expression upon KAT2A depletion. KRTAP2-3 has been linked to TGFβ-induced cell cycle arrest (31). Conversely, genes associated with the regulation of stem cell properties ( EPHB2 , SOX12 , SOX13) (32,33) were downregulated.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, another keratin gene, KRTAP2-3, also exerted increased expression upon KAT2A depletion. KRTAP2-3 has been linked to TGFβ-induced cell cycle arrest (31).…”

Section: Crispr-cas9-mediated Knockout Of Kat2a Diminishes Proliferat...mentioning

confidence: 99%

The histone modifier KAT2A presents a selective target in a subset of well-differentiated microsatellite-stable colorectal cancers

Kufrin,

Seiler,

Brilloff

et al. 2023

Preprint

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BackgroundLysine acetyltransferase 2A (KAT2A) plays a pivotal role in epigenetic gene regulation across various types of cancer. In colorectal cancer (CRC), upregulation of KAT2A is associated with a more aggressive phenotype. Our study aims to elucidate the molecular underpinnings ofKAT2Adependency in CRC and assess the consequences ofKAT2Adepletion.MethodsWe conducted a comprehensive analysis by integrating CRISPR-Cas9 screening data with genomics, transcriptomics, and global acetylation patterns in CRC cell lines to pinpoint molecular markers indicative ofKAT2Adependency. Additionally, we characterized the phenotypic effect of a CRISPR-Cas9-mediatedKAT2Aknockout and chemical inhibition of KAT2A in CRC cell lines and patient- derived 3D spheroid cultures.ResultsOur findings reveal thatKAT2Adependency is closely associated with a lower mutational burden and increased differentiation grade in CRC cell lines, independent of theKAT2Aexpression levels.KAT2Adependent CRC cell lines display enriched H3K27ac marks at gene loci linked to enterocytic differentiation. Loss ofKAT2Aleads to decreased cell growth and viability, downregulation of proliferation- and stem cell-associated genes, and induction of differentiation markers.ConclusionA specific subset of CRCs with a more differentiated phenotype relies on KAT2A. For these CRC cases, KAT2A might represent a promising novel therapeutic target.

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“…This low correlation coefficient indicates considerable variability in gene expression among cells. Oral cancer cells exhibit heterogeneous responses to transforming growth factor β stimuli via multiple EMT pathways [46]. This emphasizes the critical role of heterogeneous signaling states in affecting drug response, which should be incorporated into future modeling approaches.…”

Section: Discussionmentioning

confidence: 99%

Identifying key regulatory genes in drug resistance acquisition: Modeling pseudotime trajectories of single-cell transcriptome

Iida,

Okada

2024

Preprint

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Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560-680 genes out of 6,000 exhibiting multistable expression states in each subpopulation, including key estrogen receptor-positive breast cancer cell survival genes, such asRPS6KB1. Bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer.

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TGF-β generates a population of cancer cells residing in G1 phase with high motility and metastatic potential via KRTAP2-3

Cited by 18 publications

References 52 publications

Population Dynamics of EMT Elucidates the Timing and Distribution of Phenotypic Intra-tumoral Heterogeneity

Population Dynamics of EMT Elucidates the Timing and Distribution of Phenotypic Intra-tumoral Heterogeneity

The histone modifier KAT2A presents a selective target in a subset of well-differentiated microsatellite-stable colorectal cancers

Identifying key regulatory genes in drug resistance acquisition: Modeling pseudotime trajectories of single-cell transcriptome

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